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1. Introduction 
The rapid growth of new modes of electricity production and demands, allows the development of new services in 
different domains, such as two-way flows of electricity and information, electric vehicles, and energy efficient 
buildings. However, new intelligent energy delivery management architectures to ensure reliable operations are 
required. In fact, power grid network differs from other network systems because the electric power is not stored and 
its generation capacity and delivery are statically scheduled and tailored to given and expected demands. It is 
therefore necessary to develop new decentralized control approaches, taking into account infrastructure constraints, 
* Corresponding author. Tel.: [phone]; fax: [phone]. 
E-mail address: [e-mail] 

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implementation and production capacity to meet demands’ fluctuations, which could not be entirely predictable. 
Furthermore, the integration of electric vehicles in the power grid network generates new additional issues that need 
to be also considered. 
In the past few years, great research efforts have been dedicated to developing the power engine of electric 
vehicles and batteries. However, little attention has been paid so far to their charging process and infrastructure. This 
is due to their charging process, which is completely different from the refueling process of conventional engines 
powered vehicles. Mainly, the uncertainty of drivers to get suitable and vacant places at a charging station 
constitutes one of the major obstacles to the large deployment of electric vehicles [1]. Recently, several scheduling 
and assignment approaches have been proposed to tackle this issue ([2], [3], [4] and [5]). For example, the 
charging/discharging process has been formulated in [3] as a global scheduling optimization problem, in which 
powers of charging are considered to minimize the total cost of all EVs. Authors in [4] propose a distributed 
scheduling approach for minimizing the waiting time for EV charging in large-scale road networks.
In our previous work, an assignment approach for charging EVs is proposed in [6] and [7]. It can be used to 
predict the charging rate and charging time for EVs requests. A Time Event Graph-based model (TEG) was 
proposed to describe the behavior of the system components. This model is basically used to study some qualitative 
properties of the system. In order to complete this study and evaluate some quantitative properties of the system, a 
(max, +) - model derived from the TEG model, was developed and analyzed. This model allows expressing and 
studying the system behavior. The work presented in this paper introduces a predictive function-based model for 
handling multiple charging demands and predicting average charging rates and charging times. The main aim is to 
minimize simultaneously the waiting time of each received request and the occupation time of charging stations.
The remainder of this paper is organized as follows. Section 2 presents a survey of exiting work from literature. 
Section 3 is dedicated to the description of the trade-off approach based on the introduced predictive function. In 
Section 4, we present the predictive approach with obtained simulations results. The last section concludes the paper 
and gives some future research directions.